@inproceedings{flanigan-etal-2022-meaning,
title = "Meaning Representations for Natural Languages: Design, Models and Applications",
author = "Flanigan, Jeffrey and
Jindal, Ishan and
Li, Yunyao and
O{'}Gorman, Tim and
Palmer, Martha and
Xue, Nianwen",
editor = "El-Beltagy, Samhaa R. and
Qiu, Xipeng",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = dec,
year = "2022",
address = "Abu Dubai, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-tutorials.1",
doi = "10.18653/v1/2022.emnlp-tutorials.1",
pages = "1--8",
abstract = "This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We will also present qualitative comparisons of common meaning representations and a quantitative study on how their differences impact model performance. Finally, we will share best practices in choosing the right meaning representation for downstream tasks.",
}
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%0 Conference Proceedings
%T Meaning Representations for Natural Languages: Design, Models and Applications
%A Flanigan, Jeffrey
%A Jindal, Ishan
%A Li, Yunyao
%A O’Gorman, Tim
%A Palmer, Martha
%A Xue, Nianwen
%Y El-Beltagy, Samhaa R.
%Y Qiu, Xipeng
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dubai, UAE
%F flanigan-etal-2022-meaning
%X This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We will also present qualitative comparisons of common meaning representations and a quantitative study on how their differences impact model performance. Finally, we will share best practices in choosing the right meaning representation for downstream tasks.
%R 10.18653/v1/2022.emnlp-tutorials.1
%U https://aclanthology.org/2022.emnlp-tutorials.1
%U https://doi.org/10.18653/v1/2022.emnlp-tutorials.1
%P 1-8
Markdown (Informal)
[Meaning Representations for Natural Languages: Design, Models and Applications](https://aclanthology.org/2022.emnlp-tutorials.1) (Flanigan et al., EMNLP 2022)
ACL